Predictive Maintenance Approach for Complex Equipment Based on Petri Net Failure Mechanism Propagation Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of this paper is to propose a comprehensive approach for predictive maintenance of complex equipment. The approach relies on a physics of failure model based on expert knowledge. The model can be represented as a multi-state Petri Net where different failure mechanisms have been discretized using physical degradation states. Each state can be detected by a unique combination of symptoms that can be measured from diagnostic tools. Based on actual existing diagnostic information, a diagnostic algorithm enables the identification of active failure mechanisms and estimates their progression in the Petri Net. Specific maintenance actions and their potential effect on the system can be associated with targeted states. Thereafter, a prognostic algorithm using a coloured Petri Net propagation method spreads active failure mechanisms though their related remaining states towards the targeted states. This allows specific maintenance actions to be proposed in a timeframe and thus enables predictive maintenance. Case study is presented for a real hydro generator. Finally, model limits are discussed and potential areas of further research are identified.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it